2 10 33 https://repository.horizon.ac.id/files/original/7d3c07442f3403f16e31d8f83c7c32c6.pdf 828c14bd5ba79a7d17cb41391a0ae934 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Vol 9 No 5 (2025) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Comparing Data Preprocessing Strategy on T5 Architecture to Classify ICD-10 Diagnosis Subject The topic of the resource ICD-10 classification; medical NLP; preprocessing strategy; T5; text-to -text transforme Description An account of the resource Manual ICD-10 coding in healthcare systems remains time-consuming, error-prone, and inefficient, particularly in resource-constrained settings. This study investigates the effect of various preprocessing strategies on the performance of the Text-to-Text Transfer Transformer (T5) model for primary diagnosis classification using structured clinical data. A total of 7,263 clinical records were collected from two high-density regions in Bali (Badung and Gianyar) between January 2023 and March 2024, then converted into descriptive text prompts for model training. Four experimental scenarios combined variations of input features and label configurations, comparing T5 with Oversampling against T5 with Easy Data Augmentation (EDA) plus Oversampling. Results showed that T5 with Random Oversampling consistently outperformed the EDA-based configuration across all scenarios, with performance gaps ranging from 8% to 19%. Scenario 4, which excluded body system features and the semantically overlapping E860 label, achieved the highest balance, reaching 84.7% accuracy, 85.1% precision, 84.7% recall, and 84.3% F1-score. Conversely, the EDA-based approach reduced training time by up to 72%, indicating a clear trade-off between performance and efficiency. Both configurations frequently misclassified semantically similar codes within the same ICD-10 categories, underscoring the difficulty of distinguishing clinically related diagnoses. Overall, the results suggest that careful selection of preprocessing strategies can enhance transformer-based medical text classification, while striking a balance between model performance and training efficiency. This work may serve as an initial reference for developing more efficient semi-automated medical coding systems in the Indonesian regional healthcare contex Creator An entity primarily responsible for making the resource I Gusti Ngurah Lanang Wijayakusuma1*, Made Sudarma2, I Ketut Gede Darma Putra3, Oka Sudana4, Ni Putu Dian Astutik5 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6919/1149 Publisher An entity responsible for making the resource available Department of Doctoral Engineering, Faculty of Engineering, Universitas Udayana, Bali, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource October 25, 2025 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/9f047394976d39e3b3f5fc702506f00e.pdf 82cb736ded51dd0e8ad0a4ec67a26428 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Vol 9 No 5 (2025) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Eye Disease Detection and Classification Optimization Using EfficientNet-B5 with Emphasis on Data Augmentation and Fine-Tuning Subject The topic of the resource Automated Diagnosis; Data Augmentation; EfficientNet-B5; Eye Disease Detection; Fine-tuning Description An account of the resource ye diseases significant global health challenges, underscoring the need for efficient and accurate diagnostic. This study employed the EfficientNet-B5 model to enhance the detection and classification of eye diseases by incorporating advanced data augmentation and fine-tuning techniques. The research utilizes the ODIR dataset, consisting of 4,217 fundus images categorized into four classes: normal, glaucoma, cataract, and diabetic retinopathy. The methodology comprises three phases: baseline model training, model training with data augmentation, and fine-tuning. The baseline model achieved an accuracy of 60.43%, which improved to 63.03% with data augmentation an increase of 2.6 percentage points. Fine-tuning further elevated the accuracy to 93.23%, representing a notable improvement of 33.8 percentage points over the baseline. Model performance was evaluated using standard classification metrics. These findings demonstrate the technical efficacy of combining augmentation and fine-tuning to enhance model generalization. This result approach offers a robust framework for developing dependable AI-driven diagnostic tools to support early detection and facilitate informed clinical decision-making. Creator An entity primarily responsible for making the resource Anggi Muhammad Rifai1, Muhammad Fatchan2, Ahmad Turmudi Zy3, Donny Maulana4, Sufajar Butsianto5 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6519/1148 Publisher An entity responsible for making the resource available Department of informatics engineering, Faculty of engineering, Pelita Bangsa University, Bekasi, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource October 24, 2025 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/a3237ea323d04e935a24d44120fad844.pdf 82cb736ded51dd0e8ad0a4ec67a26428 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Vol 9 No 5 (2025) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Application of YOLOv8 Algorithm for Coral Reef Disease Detection as an Effort to Prevent Marine Habitat Damage in Batam Subject The topic of the resource coral; detection; disease; YOLOv8 Description An account of the resource Research in 2019 in Batam City showed that out of 19 coral reef fisheries support facilities, 16 were declared not good. Coral reef damage increased from 36.28% to 39.44%. This is due to the threat of coral reef damage due to international shipping lane areas, human activities such as destructive fishing, pollution, sedimentation, and global warming. These threats can cause coral diseases such as black band disease (BBD), brown band disease (BrB), Bleaching Coral, and yellow band disease (YBD). The Underwater Photo Transect (UPT) method collects data in the field in the form of underwater photos and analyzes them to obtain quantitative data. This method has a weakness, namely the low level of accuracy in detecting coral reef diseases. This study proposes coral reef disease detection using the YOLO model YOLO8l, YOLO8x, and YOLO8m. The results of the model evaluation test with a threshold value of 0.5 to 0.95 against the test data show that the three models can detect coral reef diseases with an accuracy of 99%. These results prove that the YOLOv8 model in this study is suitable for the real-time detection of coral reef diseases to replace the Underwater Photo Transect (UPT) method, which has low accuracy. Applying the YOLOv8 method will help Prevent Marine Habitat Damage in Batam City Creator An entity primarily responsible for making the resource Rifa’atul Mahmudah Burhan1, Refli Noviardi2, M Abrar Masril3, Firmansyah Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6062/1145 Publisher An entity responsible for making the resource available Information System, Information Technology, Batam Institute of Technology, Batam, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource October 13, 2025 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/30b26b73ef72d01187315640c6f37c0a.pdf 051066ec6cf67f8b4ae1e2373d1815a2 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Vol 9 No 5 (2025) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Image Classification of Rice Leaf Diseases with KNN Based Model using Stratified-KCV Subject The topic of the resource image classification; rice leaf diseases; rgb; stratified k-fold cross validation; vgg16 Description An account of the resource Rice is a staple food for people in the world, especially Indonesia. The rice harvest decreased in 2023, reducing harvest productivity and causing losses for farmers. Rice cultivation is often affected by diseases that hinder rice harvests. SKCV is a resampling method that performs more accurately because it can ensure that class frequencies are maintained. RGB and VGG16 are image processing methods that extract images into numerics. RGB image extraction is done by taking the average value of the red, green, and blue layers while VGG16 image extraction is done by taking the value of visual pattern features such as edges, textures, and object shapes. In this study, rice leaf diseases were classified using KNN-based models, including KNN, WKNN, CDNN, and ECDNN. This classification was performed to determine which method had better performance using SKCV and comparing the results of RGB and VGG16 image extraction. This classification also produces a comparison of SKCV and KCV results to determine the best resampling performance. The results of the analysis that have been carried out show that the ECDNN method produces the highest accuracy of 81.20% in classifying rice leaf diseases using SKCV with VGG16 extraction followed by CDNN and WKNN each at 68.80%, and KNN at 56.20% while RGB extraction only produces an accuracy of 43.8% using ECDNN and CDNN, 56.20% using WKNN, and 50% using KNN. The results of this rice leaf diseases classification analysis are expected to help farmers in increasing rice production in Indonesia Creator An entity primarily responsible for making the resource Tasya Anisah Rizqi1, Anwar Fitrianto2, Kusman Sadik Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6590/1142 Publisher An entity responsible for making the resource available Department of Statistics, School of Data Science, Mathematics, and Informatics, IPB University, Bogor, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource October 12, 2025 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/d02947dce7192348b5383b0520721498.pdf dbf91f97f5a1e0b132ff0ac0575a000c Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Vol 9 No 5 (2025) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Spatial-Temporal Analysis of Earthquakes in Indonesia with Deep Learning Models: Performance Evaluation of CNN, LSTM, and Hybrid CNN-GRU Subject The topic of the resource bidirectional LSTM; deep learning; earthquake prediction; Hybrid CNN-GRU; spatiotemporal analysis Description An account of the resource Indonesia, located along the Pacific Ring of Fire, experiences high seismic activity with over 6,000 earthquakes annually. Accurate earthquake prediction remains a major challenge because of the complexity of geological dynamics and limitations of traditional methods in capturing nonlinear seismic patterns. Although deep learning approaches have shown promise, previous studies have often treated spatial and temporal analyses separately, limiting holistic predictive performance. This study proposes a novel hybrid CNN-GRU deep learning model that integrates spatial feature extraction CNN and temporal sequence modeling GRU, and compares its performance with of that CNN, LSTM, GRU, and Bidirectional LSTM using adataset of 117,251 earthquake events in Indonesia (2008–2024). The results show that Bidirectional LSTM achieved the best temporal accuracy (R² 0.653, RMSE 0.592), while the hybrid CNN-GRU provided balanced spatial-temporal performance (R² 0.587). Notably, the performance gap between Bidirectional LSTM and other models (e.g., Hybrid CNN-GRU) was statistically validated via paired t-test (p < 0.05). The proposed models generalize well to unseen regions such as Maluku-Papua. The key contribution is the hybridization of spatial-temporal learning in a single-model architecture - where CNN processes latitude-longitude coordinates via 1D convolutions while GRU handles temporal sequences - an approach lacking in earlier works. This directly improves early warning systems in seismically active areas by providing 32% higher accuracy than conventional methods Creator An entity primarily responsible for making the resource Susandri Susandri1, Feldiansyah Bakri Nasution2, Fajrizal Fajrizal3, Saparudin4 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6538/1137 Publisher An entity responsible for making the resource available Postgraduate of Computer Science, University of Lancang Kuning, Pekanbaru, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource October 10, 2025 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/668b38b110bf700c267726247870c007.pdf dbf91f97f5a1e0b132ff0ac0575a000c Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Vol 9 No 5 (2025) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Application of Reinforcement Learning to Solve Rubrik’s Cube with Markov Decision Process Subject The topic of the resource Reinforcement Learning, Rubik’s Cube; Markov Decision Process Description An account of the resource The Rubik's Cube is a tricky puzzle that can be arranged in countless ways, making it hard for both people and computers to figure out. While standard solving methods use fixed strategies, this research looks into using reinforcement learning (RL) to create a flexible and effective way to solve it. The goal of this research is to develop an RL-based solver that uses the Markov Decision Process (MDP) system, focusing on speed, efficient moves, and the number of steps needed to solve the cube. The suggested model uses Q-learning and Monte Carlo Tree Search (MCTS) to figure out the best moves at each stage of the game, training through lots of Rubik's Cube simulations. What makes this research unique is the combination of MCTS with Q-learning, which improves decision-making by needing fewer moves than standard methods. The tests show that this model reaches almost perfect solutions with fewer moves, doing better than simple rule-based methods. Also, a web app was created to give live solving techniques based on the cube arrangements that users provide. This research helps grow the use of RL in puzzles like the Rubik's Cube and gives a useful tool for fans who want to get better at solving the cube Creator An entity primarily responsible for making the resource Defni1, Andi Fathul Mukminin2, Ainil Mardhiah3, Titin Ritmi4, Junaldi5, Yuhefizar6, Fibriyanti7 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6552/1135 Publisher An entity responsible for making the resource available Information Technology, Padang State Polytechnic, West Sumatera, Indonesi Date A point or period of time associated with an event in the lifecycle of the resource October 8, 2025 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/c426f4c2c4b21bef5966002255c89976.pdf dbf91f97f5a1e0b132ff0ac0575a000c Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Vol 9 No 5 (2025) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Software Product Line Engineering in Supply Chain Management Systems for Manufacturing Sector Subject The topic of the resource delta-oriented programming; software product line engineering; supply chain management Description An account of the resource Manufacturing companies are industrial enterprises that process raw materials and implement Supply Chain Management (SCM). SCM encompasses three stages: material management, planning and control, and production. While these stages are common across manufacturing companies, the workflows and strategies employed vary based on the type of goods produced. For example, one company typically approaches process orders based on requests, whereas the other processes orders based on stock availability. To address these similarities and differences, a software product line engineering (SPLE) approach can be utilized to develop SCM systems. This approach has already been proven effective in other cases, such as developing various product specifications for our Crowdfunding Application (Amanah CS UI) partner. SPLE follows the principle of mass customization, analyzing the commonalities and variabilities of the SCM system to meet diverse company needs. This approach improves the cost optimization and time efficiency in developing various SCM specifications to fulfill the requirements of each company. The development of the SCM system in this study adopts a delta-oriented programming paradigm and Abstract Behavioral Specification programming language. Subsequently, a comparison was made between the development of the SCM system using the SPLE approach and the clone-and-own approach. The research results in an enhanced SCM system developed through the SPLE, establishing it as the primary solution to existing development issues: reusing shared components and adding new custom components. Additionally, it includes an analysis that compares the SPLE approach with the clone-and-own method. Creator An entity primarily responsible for making the resource Jehian Norman Saviero1, Muhammad Raihan2, Oman Komarudin3, Ade Azurat Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/5605/1134 Publisher An entity responsible for making the resource available Faculty of Computer Science, Universitas Indonesia, Depok, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource October 3, 2025 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/3d74f4b0b0ee798524bd2c99d6843f36.pdf 2c70c049549298a9ef145dc7b393d81e Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Vol 9 No 5 (2025) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Enhancing Recruitment Efficiency in the Indonesian Pharmaceutical Industry Using Backpropagation Neural Networ Subject The topic of the resource backpropagation; deep learning; employee selection model; multi-layer perception; recruitment Description An account of the resource PT K-24 Indonesia is one of the foremost companies in Indonesia, with a primary focus on distributing pharmaceutical products and healthcare services. Over the last two years, PT K-24 has received more than 110,000 job applicants, offering various position vacancies. The recruitment process began with registrations, online tests, and interviews. The need for manpower has increased annually in recent years. More attention is required when selecting prospective employees who meet the selection criteria. However, PT K-24 found that the recruitment process was less efficient because the applicants did not meet the company’s criteria. To overcome this problem, it is necessary to create an employee selection model for candidate selection. This study developed a recommendation using the multilayer perceptron method, namely, backpropagation. The results of this study add to the confidence of prior research that the backpropagation algorithm is effective for prediction and reliable forsimilar prediction tasks. This study found that using 112,397 relevant data, 23 input parameters, 1 hidden layer containing 10 neurons, ReLu activation function, and Adam solver produced better prediction, with an MSE of 0.029. Our study contributes to the methodological approach by implementing real-world problems and measuring specific criteria relevant to the selection process. Creator An entity primarily responsible for making the resource Apfia Normalita1, Jong Jek Siang2*, Halim Budi Santoso Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6303/1133 Publisher An entity responsible for making the resource available Department of Information Systems, Duta Wacana Christian University, Yogyakarta, Indonesia Contributor An entity responsible for making contributions to the resource October3, 2025 Format The file format, physical medium, or dimensions of the resource pdf Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/16835ce3a54a59cec49256e69753108f.pdf 621b84190625e3f8cfb1cd6b00961d21 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Vol 9 No 5 (2025) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Explainable Ensemble Learning Framework with SMOTE, SHAP and LIME for Predicting 30-Day Readmission in Diabetic Patients Subject The topic of the resource diabetes; ensemble learning; explainable AI; machine learning; readmission prediction; SMOTE Description An account of the resource Hospital readmission among diabetic patients poses a significant burden on healthcare systems due to its frequency and associated costs. This study presents a machine learning framework for predicting 30-day readmission in diabetic patients using the Diabetes 130-US Hospitals dataset. The framework integrates data preprocessing, SMOTE for class balancing, ensemble learning, and explainable AI (SHAP and LIME) to enhance both accuracy and interpretability. Multiple models were evaluated, and the best performance was achieved by a weighted ensemble with a recall of 89.43% and an F1-score of 0.6612, indicating strong sensitivity. Explainability analysis using SHAP and LIME highlighted key predictors, notably Medication Change Status and Inpatient Admissions, which are clinically relevant. By combining predictive performance with transparent explanations, the proposed framework offers a practical and trustworthy tool for clinical decision support in managing diabetic readmissions Creator An entity primarily responsible for making the resource Joshua Pinem1, Widi Astuti2, Adiwijaya3 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6977/1132 Publisher An entity responsible for making the resource available Informatics Study Program, School of Computing, Telkom University, Bandung, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource September 29, 2025 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/c6fd8efff6e8d226db5b88e80a1cb7ee.pdf cbe598dea6814af5110b20ecd2caa7f4 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Vol 9 No 5 (2025) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource HUBUNGAN PERSEPSI BEBAN KERJA PERAWAT DENGAN KUALITAS PENDOKUMENTASIAN ELECTRONIC RECORD MEDICAL ASUHAN KEPERAWATAN DI RUANG RAWAT INAP DAN IGD RUMAH SAKIT ISLAM KARAWANG TAHUN 2025 Subject The topic of the resource Beban Kerja, Kualitas Pelayanan, Pendokumentasian Asuhan Keperawatan. Description An account of the resource beban kerja perawat merupakan salah satu faktor yang berpotensi memengaruhi kualitas pendokumentasian asuhan keperawatan. pendokumentasian yang tidak lengkap dan tidak tepat waktu dapat berdampak pada mutu pelayanan serta keselamatan pasien. penelitian ini bertujuan untuk mengetahui hubungan persepsi beban kerja perawat dengan kualitas pendokumentasian electronic medical record asuhan keperawatan di ruang rawat inap dan instalasi gawat darurat rumah sakit islam karawang tahun 2025. penelitian ini menggunakan desain kuantitatif deskriptif analitik dengan pendekatan cross sectional. jumlah sampel sebanyak 88 perawat yang diambil menggunakan teknik total sampling. instrumen penelitian berupa kuesioner persepsi beban kerja perawat dan lembar observasi kualitas pendokumentasian electronic medical record asuhan keperawatan. analisis data dilakukan secara univariat dan bivariat menggunakan uji chi-square. hasil penelitian menunjukkan adanya hubungan antara persepsi beban kerja perawat dengan kualitas pendokumentasian electronic medical record asuhan keperawatan. perawat dengan persepsi beban kerja tinggi cenderung mengalami penurunan kualitas pendokumentasian. simpulan penelitian ini menunjukkan bahwa pengelolaan beban kerja dan dukungan sistem pendokumentasian sangat diperlukan untuk meningkatkan mutu asuhan keperawatan. Creator An entity primarily responsible for making the resource Jenny Fatimatul Azzahra1, Ns. Amella Gusty, M. Kep2 , H. Agus Subari, M.Kes3 Source A related resource from which the described resource is derived https://mail.google.com/mail/u/0/#inbox?projector=1 Publisher An entity responsible for making the resource available Fakultas Ilmu Kesehatan – Universitas Horizon Indonesia Date A point or period of time associated with an event in the lifecycle of the resource FEBRUARI 2026 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT